Usage

Testing

Run
th rnnTracker.lua
to get a sense of the result on synthetic data or
th rnnTracker.lua -model_name rnnTracker -seq_name TUD-Campus

to produce results on the TUD-Campus sequence. The bounding
boxes are saved in ./out/rnnTrack/TUD-Campus.txt. Type
th rnnTracker.lua -h to get a full list of options

This example uses Hungarian data association.

Visualization

To see the visual results you can run
th visBoxes.lua -file ../out/rnnTracker_r300_l1_n1_m1_d4/TUD-Campus.txt

Training

th trainBF.lua -config ../config/configBF.txt

will start training a model on the TUD-Campus sequence. Type
th trainBF.lua -h

to see the full set of options. You may define the training parameters
in a separate text file, similar to config/configBF.txt and pass it
as the -config option to the training script.

Data

Training expects annotated image sequences. The annotation format is a CSV text file
following the syntax of the MOTChallenge benchmark.
For testing, the image sequence and the corresponding set of detection in the same
format is required.

Internal representation

Internally, all data (tracks and detections) is stored in N x F x D tensors, where

N = max. number of targets / detections

F = number of frames in a batch

D = dimensionality (e.g. 2 for (x,y) or 4 for (x,y,w,h)

The labels (data association) is represented by an NxF tensor.

Furthermore, training, validation and real-data sets are kept in a lua table.
I.e. each entry in a table is then a MB*N x F x D tensor, where MB is the mini-batch size. There are four tables for each set.

Tracks

Detections

Labels

Sequence names (used for generating that one specific datum)

Documentation

The code is documented following the luadoc convention. To generate
html docs, install luadoc luarocks install luadoc and run ./docify.sh.

Known issues

Data Association

The code for training data association is not included yet. We are working on releasing it soon.